(1) Background: the effects of load carriage packs on human gait biomechanics, physiology and metabolism depend on the weight carried, the design of the pack and its interaction with the user. (2) Methods: An extensive search in the PubMed database was performed to find all the relevant articles using the following keywords: backpack, rucksack, backpack ergonomy and sports backpack; 60 articles were included. (3) Results and significance: Double pack (DP) and T-pack (TP) designs are recommended solutions for school children, compared with backpacks (BP). For soldiers and hikers, a backpack remains the best compromise. A hip belt is recommended for BPs as well as for the back of DPs. Shorter and stiffer shoulder straps combined with a higher and tighter load placement on the back provide the best combination in terms of balance, muscle activation and energy expenditure. It is, therefore, possible to determine guidelines for designing the optimal load carriage system, depending on the application. (4) Conclusions: based on the available evidence, DP and TP are advantageous in terms of posture. DP is better than conventional BPs in terms of balance and muscle activation, but has the disadvantage of limited visibility, thermal sensation and obstructed ventilation. In general, it is desirable not to exceed 40% of body mass (BM).

This study investigates the movement of moisture and associated heat loss from the sock to the microclimate and external environment in mountain footwear. As it is not how much sweat is produced, but how much of it can evaporate, a thorough study was carried out to analyse the various contributions to heat exchange in this context. The tests were performed on a thermal manikin with a 100% cotton sock loaded with different amounts of water (0g, 5g, 15g, and 30g) at 10°C and 50% RH. Long-term tests were carried out with the maximum amount of water loaded (30g) to observe the different phases of heat and mass transfer until complete evaporation of all moisture (both from the socks and from the boots). In addition, evaporation tests were performed under semi-isothermal conditions (T _amb = T _man = T _socks ) at 34°C and 12% RH (same vapour pressure as in the cold tests) to isolate the evaporative contribution without any conductive, convective or condensing heat loss component. The actual effect of the evaporation was compared with the theoretical estimates from the mass-loss method. It was found that heat loss by evaporation was overestimated as a fraction of water evaporated from the socks underwent condensation in the boots, which does not contribute to cooling the system and returns heat to the system.

This study investigates the thermal insulation and moisture management of three types of mountaneering boots and simulated hiking activities under controlled environmental conditions with two elite athletes. Temperature and humidity were determined with six wireless probes placed on the most exposed parts of the foot (hallux, middle toe, little toe, dorsum, ankle and sole). Thermal images were taken to record the thermal insulation of each sample. Methodologically, the study aims to simulate every movement and activity of alpinism in order to realistically evaluate the conditions of use of this kind of footwear (also taking into account the lacing pressure exerted on the foot). Based on the results obtained, in a further step it will be possible to define the best solution in terms of combination of materials by creating a comfort scale for hiking boots.

Predicting the thermo-physiological comfort of technical clothing requires an understanding of how microscopic textile structures influence macroscopic properties such as air, heat, and moisture permeability. This work represents the first step towards a multi-scale predictive tool capable of estimating key comfort-related properties from the geometrical features of woven fabrics. Focusing on air permeability, the effect of structural and design parameters was investigated while keeping the fibre material (cotton) constant. A computational framework that combines validated Computational Fluid Dynamics (CFD) simulations with a Fully Connected Neural Network (FCNN) was developed, enabling fast and accurate predictions before production. The CFD model accounts for both intra- and inter-yarn porosity, ensuring reliability across a wide range of fabric configurations. The FCNN, trained on simulation and literature data, achieved a mean absolute relative error of 2.01% and a maximum error of 7.72%, demonstrating excellent agreement with experimental results. The analysis highlights how weave type and yarn density govern airflow resistance, offering an efficient tool for the design and optimisation of breathable technical textiles.